Managing lifelong learner events on a blockchain

ABSTRACT

A method of managing lifelong learner events on a blockchain includes detecting an event related to a learner using a blockchain-enabled digital learning system, determining a concern/risk level of the learner by performing a risk assessment, determining parameters to generate a transaction related the learner&#39;s event based on the parameters and the concern/risk level, determining the values of the parameters by measuring the value or importance of the event and its associated metadata and documents, generating a list of transactions corresponding to the parameters, and validating the transactions using validating distributed peer-to-peer devices that run one or more chaincodes related to the management of the lifelong learner events.

BACKGROUND Technical Field

Embodiments of the disclosure are directed to improving learning bymaintaining valid and tamper-proof educational records for teachers andstudents to monitor learning outcomes. The records include all elementsor events associated with teacher and student academic lifecycles. Theselifecycle events range from student and teacher attendance, how astudent approaches learning, including interactions with content, totardiness, in-class exams and homework, student (and teacher)discipline, etc.

Discussion of the Related Art

In the developing world, the adoption of technology to manage data willlead to cost effectiveness, efficiency, and the seamless integration ofan education management information system (EMIS). There are a widevariety of technologies that include, but are not limited to, mobilephones, tablets, laptops, desktops and broadband connectivity that canbe used in data collection, processing, analysis and dissemination ofeducational data. Furthermore, educational data informs policy, planningand management of education with the aim of improving the quality,access and the relevance of education.

However, in many cases there is insufficient data on teachers andstudents to support personalized education. Personalized education is anapproach to improving the learning experience for both teachers andstudents by fostering engaged teacher-student experiences. It requiresan understanding of the learning environment and a detailedunderstanding of the student's learning history and their performancechallenges. Outside of exam results, there is insufficient data to helpsupport widespread teacher-student engagement. Note that currentlyexisting adaptive or personalized education systems consolidate datafrom disparate sources to improve learning outcomes and student success.This poses data security and privacy issues while managing studentrecords.

A student record management system can be used to track lifelong eventsof a student in which the record is held in a database, such as a“School Information Hub”, by a central “owner.” Currently, school recordmanagement systems keep track of events related to the student recordmade by a digital learning environment or stakeholders, who stillrequire participation of internal and external actors, by “checking inthe record” to the database.

Previous approaches have focused either on centralized or decentralizedmodels of managing school records, learning and teaching activities,etc. With a centralized model, the owner of the school records may betrusted to manage operations on records and the owner of the recordsalso owns the rules. Any operation on the records is trusted by trustingthe rules to be appropriate. But, such a model can easily allow for asingle point of failure in trust. With a purely decentralized model,trust is disseminated among the many. Such a model of tracking andmanaging school systems has failed in many developing countries due toits inability to block corruption at various levels. However, adecentralized model with consensuses, using an identity authority,allows one to rely on trusted services and non-repudiation, and by usinga blockchain, immutable operations/transactions can be ensured.

This is paradoxical, in that without a central database, records may bemodified and altered by different stakeholders in different subjects asthe user, e.g., a learner, may be enrolled in more than one course, auser may switch learning modes from classroom model to outside classroommode to tutoring mode, and different stakeholders may share a recordwith each other. In this way, a student record can “evolve”independently of the primary central database, and alteration of recordsis possible.

Recently a number of initiatives are proposing the use of blockchaintechnology for education. The use of a blockchain for lifelong recordmanagement ensures that records remain consistent and can never bemaliciously modified by any individual. Instead, the alteration ofstudent records can be verified against a particularly useful instanceof the student record by obtaining from the blockchain a historicalblock identifier of a student record historical blockchainrepresentative of all historical student events that have been compiledonto the student's record. This greatly improves the accountability ortransparency (electronic audit trails) while guaranteeing data securityand privacy by design.

However, all current blockchain-based-technologies in education focus oneducational testing, academic certification and the authentication ofdegrees. They do not focus on the day to day running of a school and themyriad of events that affect a student's or teacher's academic record,such as student interactions with learning materials, such as learnerinteractions with content presented on a computer device, includingstart, pause, stop, fast forward, rewind, zoom-in, zoom-out, hover,expressed sentiments such as happy, sad, bored, etc all tagged withtimestamp and/or location, student and teacher attendance, tardiness,in-class exams and homework, student (and teacher) discipline, etc.Typically, these events and data are used by intelligent applicationsfor adaptive and personalized learning to infer learner engagement,understanding and progress or lack thereof, for example, to infercontent effectiveness and popularity, and for example, to infer learnersentiment, context and affective states.

SUMMARY

Embodiments of the disclosure are directed to the management of studentand teacher lifelong records using blockchain technology to securelytrack, maintain and manage these records. Transactions associated withthe record are compiled into a chain of record transaction blocks. Thechain can be considered a chronicle of record path through time. When atransaction occurs, one or more corresponding record parameters oflearning events, such as completion of learning content, completion ofquiz/assessment, updating knowledge model, updating mastery level, etc.,are sent to one or more validation modules. The modules establish avalidity of the transaction and generate a new block. Once the new blockhas been calculated it can be appended to the historic learner lifelongevents blockchain.

According to an embodiment of the disclosure, there is provided a methodof managing lifelong learner events on a blockchain, including detectingan event related to a learner using a blockchain-enabled digitallearning system, determining a concern/risk level of the learner byperforming a risk assessment, determining parameters to generate atransaction related the learner's event based on the parameters and theconcern/risk level, determining the values of the parameters bymeasuring the value or importance of the event and its associatedmetadata and documents, generating a list of transactions correspondingto the parameters, and validating the transactions using validatingdistributed peer-to-peer devices that run one or more chaincodes relatedto the management of the lifelong learner events.

According to a further embodiment of the disclosure, the methodincludes, when the transaction validation succeeds, creating a new blockand updating the blockchain based on the new block, and compiling thetransactions associated with the learner's computing device into a chainof the learner's event blocks.

According to a further embodiment of the disclosure, events include oneor more of learner activities, application events, and sensor events.

According to a further embodiment of the disclosure, the risk assessmentis based on the learner's observed behavior, predicted learnercharacteristics, and context.

According to a further embodiment of the disclosure, the method includesperforming one or more off-blockchain processing and analyticsprocesses, when off-blockchain processing is needed.

According to a further embodiment of the disclosure, validating thetransactions includes receiving validity requirements for events on theblockchain, obtaining a validation token indicative of the validity ofthe learner event, and computing a chaincode block for the transactionagainst the validation requirements.

According to a further embodiment of the disclosure, the method includesautomatically changing a rate of updating the distributed repositorybased on the risk assessment.

According to a further embodiment of the disclosure, the method includesrewarding a learner with a cryptocurrency based on the learner'shistoric performance, engagement and overall progression, and recordingthe reward in the distributed repository for the learner.

According to another embodiment of the disclosure, there is provided anapparatus for managing lifelong learner events on a blockchain thatincludes a distributed repository to securely store and maintain learnerrecords, an event framework that collects and aggregates learningactivities or events of a user related to content and assessment by alearning application or cognitive event using in-built in mobilesensors, a transaction detector that detects the collection of newcontent based on the output of the event framework, and a parameterprocessor that compiles the new content into a new learner record andtriggers an appending of a record transaction associated with the userinto the distributed repository, based on the new content detected bythe transaction detector.

According to a further embodiment of the disclosure, the distributedrepository is a blockchain.

According to a further embodiment of the disclosure, record transactionsassociated with a learner record include one or more of updating studentperformance data, updating user profiles, recording a learner'saffective or cognitive state, an interaction event associated withlearner's learning activity, posting comments, feedback or questions,and submitting an assessment item.

According to a further embodiment of the disclosure, triggering theappending of transactions into the distributed repository is furtherbased on one or more customizable parameters, where the customizableparameters include biometric information of a learner through the lifeof the learner record or for a period of time, activities of a learnerthrough the life of the learner record or for a period of time, one ormore actions of a learner with content presented by a learningtechnology environment on computer device, assessment or quiz specificevents, and aggregate usage data of content, location of content beingskipped, and rewinding a content location.

According to a further embodiment of the disclosure, the apparatusincludes a button on a GUI to activate the appending of a recordtransaction into the distributed repository.

According to a further embodiment of the disclosure, the distributedrepository includes a chronicle of a user's biometric informationthrough the life of the learner record, or for a predetermined period oftime, a chronicle of learner activities through the life of the learningprocess, or for a predetermined period of time, one or more learnerinteractions with content presented by a learning technology environmenton a computer device, and a history of contents viewed, where thelearner interactions include one or more of a start, pause, stop, fastforward, rewind, zoom-in, zoom-out, and hover, assessment or quizspecific events and data, including one or more of quizzes orassessments taken, time spent per question, number of responses, numberof attempts, a timestamp, grade, skipped content, and an attentionassessment, an aggregate content usage data, location of skippedcontent, location of repeated content, affective or attentive states ofa learner along with timestamp and location, where the affective orattentive states include whether the learner is happy, sad, bored, wherethe affective or attentive states are self-reported or detected by anintelligent means, ratings of skills, knowledge, progressions,attendance, and behavior, learner context information from sensor eventsthrough the life of the learner record, where sensors includes one ormore of an accelerometer, a microphone, a light sensor, and a GPS,application activity data, and one or more of formative assessment data,behavior data, location data of content usage, and other applicationactivities.

According to another embodiment of the disclosure, there is provided anon-transitory program storage device readable by a computer, tangiblyembodying a program of instructions executed by the computer to performthe method steps for managing lifelong learner events on a blockchain.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary system according toembodiments of the disclosure.

FIG. 2 depicts an exemplary blockchain network for a user according toan embodiment of the disclosure.

FIGS. 3A-B is a flowchart of a workflow according to an embodiment ofthe disclosure.

FIG. 4 is a schematic of an exemplary cloud computing node thatimplements an embodiment of the disclosure.

FIG. 5 shows an exemplary cloud computing environment according toembodiments of the disclosure.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Exemplary embodiments of the disclosure as described herein generallyprovide systems and methods for the management of learner lifelongrecords. While embodiments are susceptible to various modifications andalternative forms, specific embodiments thereof are shown by way ofexample in the drawings and will herein be described in detail. Itshould be understood, however, that there is no intent to limit thedisclosure to the particular forms disclosed, but on the contrary, thedisclosure is to cover all modifications, equivalents, and alternativesfalling within the spirit and scope of the disclosure.

Embodiments of the disclosure use blockchain technology to securelytrack, maintain and manage lifelong learner records, where a learner canbe either a student or a teacher. Transactions associated with therecord are compiled into a chain of record transaction blocks. The chaincan be considered a chronicle of record path through time. When atransaction occurs, one or more corresponding record parameters oflearning events, such as completion of learning content, completion ofquiz/assessment, updating knowledge model, updating mastery level, etc,are sent to one or more validation modules. The modules establish avalidity of the transaction and generate a new block. Once the new blockhas been calculated it can be appended to the historic learner lifelongevents blockchain.

According to embodiments, a transaction related to a learner recordincludes events such as recording, updating or uploading learnerperformance, updating a user profile, logging a learner'saffective/cognitive state of the learner, and interaction eventsassociated with learner learning activity, posting a comment, feedbackor question, submitting or completing an assessment item, etc. Updatinga user profile includes, for example, knowledge model, skill model, userpreference/style, etc. Learner learning activities include content iteminteractions, assessment/quiz item interactions, content searching,e.g., in adaptive learning platforms such as Cognitive LearningCompanion™ (CLC), and the mEduPAL™ application.

The Cognitive Learning Companion™ (CLC), and the mEduPAL™ have beendeveloped by IBM Research to connect multiple modes of learning, enabledthrough mobile platforms and delivered via the cloud and support thecollection, storage and processing of dynamic user interactions fromsystems like CLC, respectively.

An event can be one or more actions of a learner, such as: interactionwith content presented by a learning technology environment on acomputing device, such as start, pause, stop, fast forward, rewind,zoom-in, zoom-out, hover, history of contents viewed, etc.;assessment/quiz specific events such as quiz/assessment taken, timespent per question, number of responses, number of attempts, etc., alongwith a timestamp, gradebook data, skipping content, lack of attention oncontent, aggregate usage data of content, location of content beingskipped, rewind content location; a learner's affective or attentionstates, either self-reported or detected by intelligent means such ascamera data, such as happy, sad, bored, along with timestamp and/orlocation, such as in classroom, outside classroom, as captured by aninstrumented environment; events related to ratings of skills,knowledge, progressions, attendance, behavior, etc., learner contextualinformation from sensor events. An event conducted with respect to aninstance of the learner record refers to the compilation of the all ofthe above events onto a particular learner record on a blockchain.

According to embodiments, any transaction related to a learner recordshould include the universally unique identifier (UUID) of the learner.Various customized parameters related to various records, such aslearner, teacher, resource, etc., records, can be added to the growingblock, including:

-   -   A chronicle of biometric information of a user through the        “life” of the learner record, or for a period of time T;    -   A chronicle of activities of a learner through the “life” of the        learning process, or for a period of time T;    -   One or more actions of a learner interaction with content        presented by a learning technology environment on a computer        device, such as a mobile device, a tablet, an iPad, a desktop,        etc., such as start, pause, stop, fast forward, rewind, zoom-in,        zoom-out, hover, history of contents viewed, etc.;    -   Assessment/quiz specific events such as quiz/assessment taken,        time spent per question, number of responses, number of        attempts, etc., along with a timestamp, Gradebook data, skipping        content, lack of “attention” on content, etc.;    -   Aggregate usage data of content, location of content being        skipped, rewind a content location;    -   One or more affective or attention states of a learner, either        self-reported or detected by intelligent means such as camera        data, such as happy, sad, bored, along with timestamp and/or        location, e.g. in classroom or outside classroom context, as        captured by instrumented environment;    -   The learner record's various entities though life, such as        stored as IDs' of entities such as course IDs, assessment ID,        school ID, etc.;    -   Ratings of skills, knowledge, progressions, attendance,        behavior, etc.;    -   One or more learner context information from sensor events such        as an accelerometer, a microphone, a light sensor, a GPS etc.,        through the life of the learning progression;    -   One or more application activity data; and    -   One or more of formative assessment data, behavior data,        location data of content usage, other application activity, etc.

According to embodiments, one or more of these parameters can besecurely stored and managed in a growing block. Embodiments canimplement various utilities for tracking, storing and managing recordsbased on events related to learning activities. Further embodiments canintelligently sense and process learner's interaction data, such aslearning interaction data on devices in which the management of sensedevent data include progressive event summarizations and similarity-basedreconstructions from historical logs on the blockchain.

Learner events are produced by the use of learning technologies, andthese events can also be stored and managed on a blockchain. Forexample, upon completion of learning content or completion of quizassessment, a system according to an embodiment may decide to includeevents that have been captured or sensed during the user's interactionwith learning technology system and persist these events on thelearner's historic record blockchain. The decision to include one ormore parameters, including metadata, in the blockchain block can bedetermined in real-time based on an arbitrary context and on theimportance of the collected parameter, such as topic or concept of thecontent.

FIG. 1 is a block diagram of an exemplary system according toembodiments of the disclosure. Referring now to the figure, a systemaccording to embodiments includes a learner record that includes one ormore of a learner profile, learner attendance, etc., a preprocessormodule 11 in communication with a digital learning environment 10, andalso in communication with a blockchain network 12. The blockchainnetwork 12 is connected to a blockchain learner lifelong store 13 thatserves as a database for storing the chaincode and records of theblockchain network 12. The learner record is stored on the blockchainlifelong learner store 13. A preprocessor according to embodiments ofthe disclosure can determine the interesting/novel quantities of eventsrelated to learning being stored in the block, determines how and whento send the content to the block, e.g. via a GUI interface to a learneror triggered by automated means; determines the rate of sendinginformation to the block, which, for example, may speed up when useful,such as when risk is high and there is a need to store more information.The preprocessor module 11 further includes an event framework 111, atransaction detector 112, a learning analytics module 113, a GUIcontroller 114, a content/parameter processor/composer 115, and acontent manager 116. According to embodiments, risk parameters are oftenstored in the block. A preprocessor according to an embodiment canchange what is actually stored in the block, based on predeterminedcriteria. For example, if risk is low, event A can be stored in theblock, but when risk is high, more of event B is stored in the block.

An event framework 111 according to an embodiment is an adaptive modulethat instruments, collects and aggregates learning activities or eventsof a user/learner related to content and assessment by a learningapplication, affective or cognitive event using in-built in mobilesensors, etc. An exemplary, non-limiting event framework is described inU.S. Patent Publication No. US2017/0078169, the contents of which areherein incorporated by reference in their entirety. A transactiondetector 112 according to an embodiment is a module that detects theaddition of content to the record based on the output of the eventframework, such as user interaction events captured by a digitallearning environment, learning progression for a period of time T, etc.A parameter processor and composer 115 is a module that compilescontents and parameters and then triggers the appending of recordtransactions associated with a learner into the chain of a historiclearner's record blockchain, based on new content detected by thetransaction detector 112. A learning analytics module 113 is acollection of various learning analytics models trained using dynamicdata captured from event framework and static data, such ashistorical/longitudinal learner data.

The content manager 116 manages learning content on the learner device,such as multimedia content such as audio, video, or text content, andthe GUI controller 114 controls and maintains the relevant UI elementsper the personalized needs of each learner.

A learner lifelong record according to an embodiment is managed andstored on the ledger, which is stored in the blockchain learner lifelongstore 13. The preprocessor module 11 collects, using the event framework111, and prepares, customizes and/or generates transactions using thetransaction detector 12 and data associated with a learner, such as datarelated to behavioral and learning events, user profile information suchas knowledge model, skill model, user preference/style, etc,affective/cognitive state of the learner, interaction events associatedwith activities such as content item interaction, assessment/quiz iteminteraction, content searching, posting a comment, feedback or question,submitting or completing an assessment item, etc. The collection andinstrumentation of learner events is performed by the event framework111. These transactions and data are then transmitted to the blockchainto be stored and securely managed on the ledger.

FIG. 2 depicts an exemplary blockchain network for a user according toan embodiment. A blockchain network is a collection of smart contractsthat can manage lifelong learner events. Referring now to the figure, ablockchain network according to embodiments includes a plurality oflearner nodes 21.1, 21.2, . . . , 21.N, one for each educationalinstitution associated with the user, and a user node 22. Each nodeincludes chaincodes and an associated ledger. Item 23 depicts a genesislearner block of a ledger, which is the very first block in a ledgerchain. The ledger chain is a linked list, with each block pointing toits successor. Examples of chaincodes include learning event processmanager chaincode, learner lifelong record manager chaincode, documentmanager chaincode, risk assessment chaincode, and an access control(ACL) chaincode that controls privileges and access rights of a schoolrecord. These chaincodes are deployed at each node of the blockchainnetwork to manage lifelong learner events. According to embodiments, allfunctionalities and services described above are governed by thenecessary smart contracts, such as process chaincode for ensuring thecorresponding processes are followed and agreed up on by all the nodes,access control chaincode that defines and manages access rights, i.e.,who can access, what can be accesses, how data is accessed,granting/revoking access rights, etc., for learner data. Each learnerledger block also includes transaction records related to one or more ofinteraction with content presented by a learning technology environment,data from built-in sensors, such as GPS, a camera, etc., or eventsrelated to the rating of skill, knowledge, progression, attendance,behavior, etc.

A flowchart of a workflow according to an embodiment of the disclosureis shown in FIGS. 3A-3B. Referring now to FIG. 3A, a workflow begins atstep 302 when a blockchain-enabled digital learning system according toan embodiment with a plug-and-play event framework such as thatdisclosed in U.S. Patent Publication No. US2017/0078169 that ismonitoring events detects an event related to a learner, such as alearner activity, an application event, a sensor event, etc. At step303, a concern/risk level of the learner is determined by performingrisk assessment related to learner's observed behavior, predictedlearner characteristics, and context concerns, etc. At step 304, theevent framework determines whether off-blockchain processing are needed,based on the context of the transaction, and if needed, the eventframework performs, at step 305, one or more off-blockchain processingand analytics processes, such as metadata extraction, fraud detection,etc. The event framework determines, at step 306, parameters to generatea transaction related to the learner based on the parameters and theconcern/risk level, and at step 307, determines the values of theparameters by measuring the values or importance of events and theirassociated metadata and documents. These parameters are related tolearner interactions, engagement, performance, sentiments, etc., and thelearner's affective and cognitive states. Parameter values includepause, start, stop, fast forward, rewind, zoom-in, zoom-out, click,hover, etc. Transactions related to a learner event include recording,updating, and uploading user profiles, where the profile includes aknowledge model, a skill model, user preferences and styles, etc.,recording a learner's affective or cognitive state based on eventsdetected from mobile sensors, interaction events associated withactivity such as content item interaction, assessment/quiz iteminteraction, content searching, posting a comment, feedback or question,submitting or completing an assessment item, learned behavioral andlearning events, etc.

Referring now to FIG. 3B, a list of transactions corresponding to theparameters is generated at step 308, which creates an auditable trail tothe extent possible. At step 309, the transactions are validated usingvalidating distributed peer-to-peer devices that run one or morechaincodes. The validation includes (1) receiving validity requirementsfor the events on the blockchain; (2) obtaining a validation tokenindicative of the validity of the learner events; and (3) computing achaincode block for the transaction against the validation requirements.At step 310, it is determined whether the transaction validationsucceeded. If, at step 311, the transaction validation succeeds, a newblock is created and the blockchain ledger is updated based on the newblock, and the transactions associated with the learner's computingdevice are compiled into a chain of the learner's event blocks at step312. If the transaction validation fails, the transaction processterminates.

In some embodiments, the rate of addition to the blockchain canautomatically change, but generally increases, based on a riskassessment or forecast, in which the risk assessment includes predictingthe learner's risk of failure on a particular topic/course, detectinghigh disengagement based on content interaction, historic behavioral oraffective patterns, etc. The rate may change in a setting where somekind of risk or forecast suggests that a learner is at risk of failing acourse, etc., based on, e.g., the performance of a learner is droppingas inferred from past assessments. This may also depend on thedependence of the learning topic or concept on content being browsed,e.g., topics in which the learner struggled on the prerequisites.

In other embodiments, the rate of addition to the blockchain canautomatically change, but again, generally increases, the more a learnerseems to exceeding the norms or averages for the particular course. Suchlearners may be at risk for disengagement due to boredom if they do notreceive targeted, more advanced, material, and also may be targets forpersonalized education when the additional data will help inform moreone-on-one teacher-learner interactions.

In further embodiments, a GUI button can be used to control what andwhen something is stored in the block, or these kinds of additions tothe block can be automated. For example, when the button on a digitalpersonalized learning system is selected or automatically enabled, ablock can be written to the blockchain, along with a Content or ResourceID, a Device ID, a content dependency graph (CDG) ID, etc. Note that asdisclosed above, any transaction related to the user record includes theUUID of the user.

A blockchain based system and method according to embodiments of thedisclosure can facilitate customized utilities by using prior art inlearning science such as learner behavior analysis, learner retentionanalytics, personalized and aggregated root cause analysis for learnerperformance, etc., and APIs for learner data insights with datapush/pull, analytics functions, etc. Thus, any (authorized) applicationlevel analytics service for personalized intervention can use a historiclearner blockchain according to an embodiment of the disclosure.

Analytics functions include content-level interactions analysis for theindividual learner to understand how a learner is engaged with learningresources, such as a learner's performance on various (sub-) strands oftests quizzes and assessments with respect to skills, knowledge andunderstanding, with results accessible to the classroom teacher,head-teacher and/or administration; item-level analysis for theindividual learner, to allow teachers to analyze whether the learnermisses the same quiz question items, and then to adjust interventionstrategies; and analytics on emotion and context of a learner'ssocial-learning-interactions behaviors to understand the learner's mood,affect, and/or cognitive states, and thus to adjust their interventionstrategies by applying various intervention strategies.

In addition, a blockchain based system and method according toembodiments of the disclosure can enable the customization ofpersonalized education services with advanced personalized insights andexperiences, personalized tutoring, learning experience management,certifications and credential management, performance and retention riskanalysis, etc.

In addition, systems according to embodiments can integrate the use ofbitcoin or other cryptocurrency technologies to reward learners based ontheir historic performance, engagement and overall progression. Coins ofthe cryptocurrency can be rewarded and stored in the learner's historiclifelong record blockchain. The historic rewarding may be used for otherpurposes, such as requesting higher education loans, demonstrating jobachievement on interviews, etc.

A blockchain based system and method according to embodiments of thedisclosure can be used to facilitate any of:

Lock In Attribution: Embodiments can help create a permanent andunbreakable link between the learner (or data owner) and the record, andthat link—the record of ownership—can be forever verified and tracked.Secure Sharing: Embodiments can help securely share one's digitalcontent with others. Sharing behavior or lifelong gains in skill is madeas easy as sharing or copying a piece of data with blockchain basedconsent management logic.Improvement Visibility: Embodiments can sometimes help trace when andhow learning or performance improves. Embodiments can show how content,topics, levels of competence, or learning routine have appeared andgrown over time.Certificate of Authenticity: Each logged behavioral, learning, etc.,event may come with a certificate of authenticity (COA), a built inunique cryptographic ID, and complete ownership history. The COA can beverified anytime and printed out.

System Implementations

It is to be understood that embodiments of the present disclosure can beimplemented in various forms of hardware, software, firmware, specialpurpose processes, or a combination thereof. In one embodiment, anembodiment of the present disclosure can be implemented in software asan application program tangible embodied on a computer readable programstorage device. The application program can be uploaded to, and executedby, a machine comprising any suitable architecture. Furthermore, it isunderstood in advance that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present disclosure are capable of being implementedin conjunction with any other type of computing environment now known orlater developed. An automatic troubleshooting system according to anembodiment of the disclosure is also suitable for a cloudimplementation.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based email). Theconsumer does not manage or control the underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with the possible exception oflimited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting for loadbalancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 4, a schematic of an example of a cloud computingnode is shown. Cloud computing node 410 is only one example of asuitable cloud computing node and is not intended to suggest anylimitation as to the scope of use or functionality of embodiments of thedisclosure described herein. Regardless, cloud computing node 410 iscapable of being implemented and/or performing any of the functionalityset forth herein above.

In cloud computing node 410 there is a computer system/server 412, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 412 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 412 may be described in the general context ofcomputer system executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 412 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 4, computer system/server 412 in cloud computing node410 is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 412 may include, but are notlimited to, one or more processors or processing units 416, a systemmemory 428, and a bus 418 that couples various system componentsincluding system memory 428 to processor 416.

Bus 418 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnect (PCI) bus.

Computer system/server 412 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 412, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 428 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 430 and/or cachememory 432. Computer system/server 412 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 434 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 418 by one or more datamedia interfaces. As will be further depicted and described below,memory 428 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the disclosure.

Program/utility 440, having a set (at least one) of program modules 442,may be stored in memory 428 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 442 generally carry out the functionsand/or methodologies of embodiments of the disclosure as describedherein.

Computer system/server 412 may also communicate with one or moreexternal devices 414 such as a keyboard, a pointing device, a display424, etc.; one or more devices that enable a user to interact withcomputer system/server 412; and/or any devices (e.g., network card,modem, etc.) that enable computer system/server 412 to communicate withone or more other computing devices. Such communication can occur viaInput/Output (I/O) interfaces 422. Still yet, computer system/server 412can communicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 420. As depicted, network adapter 420communicates with the other components of computer system/server 412 viabus 418. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 412. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 5, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 400 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 400 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 5 are intended to be illustrative only and that computing nodes900 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

While embodiments of the present disclosure has been described in detailwith reference to exemplary embodiments, those skilled in the art willappreciate that various modifications and substitutions can be madethereto without departing from the spirit and scope of the disclosure asset forth in the appended claims.

What is claimed is:
 1. A method of managing lifelong learner events on ablockchain, comprising the steps of: detecting an event related to alearner using a blockchain-enabled digital learning system; determininga concern/risk level of the learner by performing a risk assessment;determining parameters to generate a transaction related the learner'sevent based on the parameters and the concern/risk level; determiningthe values of the parameters by measuring the value or importance of theevent and its associated metadata and documents; generating a list oftransactions corresponding to the parameters; and validating thetransactions using validating distributed peer-to-peer devices that runone or more chaincodes related to the management of the lifelong learnerevents.
 2. The method of claim 1, further comprising, when thetransaction validation succeeds: creating a new block and updating theblockchain based on the new block; and compiling the transactionsassociated with the learner's computing device into a chain of thelearner's event blocks.
 3. The method of claim 2, wherein events includeone or more of learner activities, application events, and sensorevents.
 4. The method of claim 1, wherein the risk assessment is basedon the learner's observed behavior, predicted learner characteristics,and context.
 5. The method of claim 1, further comprising performing oneor more off-blockchain processing and analytics processes, whenoff-blockchain processing is needed.
 6. The method of claim 1, whereinvalidating the transactions further comprises: receiving validityrequirements for events on the blockchain; obtaining a validation tokenindicative of the validity of the learner event; and computing achaincode block for the transaction against the validation requirements.7. The method of claim 1, further comprising automatically changing arate of updating the distributed repository based on the riskassessment.
 8. The method of claim 1, further comprising rewarding alearner with a cryptocurrency based on the learner's historicperformance, engagement and overall progression, and recording saidreward in the distributed repository for said learner.
 9. An apparatusfor managing lifelong learner events on a blockchain, comprising: adistributed repository to securely store and maintain learner records;an event framework that collects and aggregates learning activities orevents of a user related to content and assessment by a learningapplication or cognitive event using in-built in mobile sensors; atransaction detector that detects the collection of new content based onthe output of the event framework; and a parameter processor thatcompiles the new content into a new learner record and triggers anappending of a record transaction associated with the user into thedistributed repository, based on the new content detected by thetransaction detector.
 10. The apparatus of claim 9, wherein thedistributed repository is a blockchain.
 11. The apparatus of claim 9,wherein record transactions associated with a learner record include oneor more of updating student performance data, updating user profiles,recording a learner's affective or cognitive state, an interaction eventassociated with learner's learning activity, posting comments, feedbackor questions, and submitting an assessment item.
 12. The apparatus ofclaim 9, wherein triggering the appending of transactions into thedistributed repository is further based on one or more customizableparameters, wherein the customizable parameters include biometricinformation of a learner through the life of the learner record or for aperiod of time, activities of a learner through the life of the learnerrecord or for a period of time, one or more actions of a learner withcontent presented by a learning technology environment on computerdevice, assessment or quiz specific events, and aggregate usage data ofcontent, location of content being skipped, and rewinding a contentlocation.
 13. The apparatus of claim 9, further comprising a button on aGUI to activate the appending of a record transaction into thedistributed repository.
 14. The apparatus of claim 9, wherein thedistributed repository further comprises: a chronicle of a user'sbiometric information through the life of the learner record, or for apredetermined period of time; a chronicle of learner activities throughthe life of the learning process, or for a predetermined period of time;one or more learner interactions with content presented by a learningtechnology environment on a computer device, and a history of contentsviewed, wherein the learner interactions include one or more of a start,pause, stop, fast forward, rewind, zoom-in, zoom-out, and hover;assessment or quiz specific events and data, including one or more ofquizzes or assessments taken, time spent per question, number ofresponses, number of attempts, a timestamp, grade, skipped content, andan attention assessment; an aggregate content usage data, location ofskipped content, location of repeated content; affective or attentivestates of a learner along with timestamp and location, wherein theaffective or attentive states include whether the learner is happy, sad,bored, wherein the affective or attentive states are self-reported ordetected by an intelligent means; ratings of skills, knowledge,progressions, attendance, and behavior; learner context information fromsensor events through the life of the learner record, wherein sensorsincludes one or more of an accelerometer, a microphone, a light sensor,and a GPS; application activity data; and one or more of formativeassessment data, behavior data, location data of content usage, andother application activities.
 15. A non-transitory program storagedevice readable by a computer, tangibly embodying a program ofinstructions executed by the computer to perform the method steps formanaging lifelong learner events on a blockchain, comprising the stepsof: detecting an event related to a learner using a blockchain-enableddigital learning system; determining a concern/risk level of the learnerby performing a risk assessment; determining parameters to generate atransaction related the learner's event based on the parameters and theconcern/risk level; determining the values of the parameters bymeasuring the value or importance of the event and its associatedmetadata and documents; generating a list of transactions correspondingto the parameters; and validating the transactions using validatingdistributed peer-to-peer devices that run one or more chaincodes relatedto the management of the lifelong learner events.
 16. The computerreadable program storage device of claim 15, the method furthercomprising, when the transaction validation succeeds: creating a newblock and updating the blockchain based on the new block; and compilingthe transactions associated with the learner's computing device into achain of the learner's event blocks.
 17. The computer readable programstorage device of claim 16, wherein events include one or more oflearner activities, application events, and sensor events.
 18. Thecomputer readable program storage device of claim 15, wherein the riskassessment is based on the learner's observed behavior, predictedlearner characteristics, and context.
 19. The computer readable programstorage device of claim 15, the method further comprising performing oneor more off-blockchain processing and analytics processes, whenoff-blockchain processing is needed.
 20. The computer readable programstorage device of claim 15, wherein validating the transactions furthercomprises: receiving validity requirements for events on the blockchain;obtaining a validation token indicative of the validity of the learnerevent; and computing a chaincode block for the transaction against thevalidation requirements.
 21. The computer readable program storagedevice of claim 15, the method further comprising automatically changinga rate of updating the distributed repository based on the riskassessment.
 22. The computer readable program storage device of claim15, the method further comprising rewarding a learner with acryptocurrency based on the learner's historic performance, engagementand overall progression, and recording said reward in the distributedrepository for said learner.